Individual Cell Motility Image Informatics

描述（申请人提供）：细胞运动是胚胎发育、免疫反应、伤口愈合、血管生成、组织工程和包括癌症转移在内的各种疾病过程的基本过程。定量单细胞运动分析提供了一个强大的工具来评估实验治疗对这些领域感兴趣的目标的影响。尽管自动化的活细胞成像系统的价格性能和可用性不断提高，但定量的单个细胞运动分析仍然是一个繁琐的，主要是手动的和不精确的过程。缺乏自动化主要是由于细胞识别能力不足，在开始的艺术图像信息学软件。一个易于使用的图像信息学工具，用于精确的细胞识别和单细胞运动分析的高度自动化分析，将加速和扩大定量细胞运动研究，影响基础研究、药物发现和疾病相关研究的许多领域。我们正在开发下一代显微镜图像信息学工具SVCell，该工具结合了显著改进的细胞识别、测量和分析技术，可用于定点分析中广泛的细胞类型。我们建议扩展SVCell的技术，以实现准确和高度自动化的基于图像的单个细胞运动分析。具体目标是：1)扩展SVCell，对延时、相位对比图像中的单个细胞进行高度自动化的动态识别；2)扩展SVCell，使其能够在集成分析环境中对动力学测量进行全面、高效的分析；3)在一个良好的基准细胞运动实验中测试扩展的SVCell。主要的创新是动态识别，它可以实现高识别精度和跟踪自动化。第二个创新是综合分析环境中的综合动力学测量，提高了运动分析结果的质量和效率。SVCell mobility将是一套开放平台图像信息应用程序之一，我们将在SVCell平台上向广泛的生命科学界推销。

DESCRIPTION (provided by applicant): Cell motility is fundamental process central to embryonic development, immune response, wound healing, angiogenesis, tissue engineering and various disease processes including cancer metastasis. Quantitative single cell motility assays provide a powerful tool to evaluate the impact of experimental treatments on targets of interest in these fields. Despite the steady improving price-performance and availability of automated, live cell imaging systems, quantitative individual cell motility assays continue to be a tedious, largely manual and inexact process. The lack of automation is primarily due to inadequate cell recognition capabilities in the start-of-the-art image informatics software. An easy to use image informatics tool for the accurate cell recognition and highly automated analyses of single cell motility assays would accelerate and expand quantitative cell motility studies, impacting many fields in basic research, drug discovery and disease related research. We are developing a next generation microscopy image informatics tool, SVCell, incorporating significantly improved cell recognition, measurement, and analysis technology for a broad range of cell types in fixed-point assays. We propose to extend SVCell's technology to enable accurate and highly automated image based individual cell motility assays. The specific aims are: 1) Extend SVCell to perform highly automated kinetic recognition of individual cells in time-lapse, phase contrast images; 2) Extend SVCell to enable the comprehensive and efficient analysis of kinetic measurements in an integrated analysis environment; and 3) Test extended SVCell in a well benchmarked cell motility experiment. The primary innovation is kinetic recognition that enables high recognition accuracy and tracking automation. A second innovation is comprehensive kinetic measurements within an integrated analysis environment that improves the quality and efficiency of motility assay outcomes. SVCell Motility will be one of a suite of open platform image informatics applications that we will market to the broad life sciences community on the SVCell platform.